An ultra-wideband bioreadar vital sign monitoring method, device, and medium

Abstract

The application discloses a kind of ultra-wideband biological radar vital sign monitoring method, equipment and medium, it is related to driver vital sign monitoring technical field, including the following steps: using ultra-wideband biological radar to emit short pulse signal to monitor driver;Signal feature is extracted to the sign of vital signs, different signal features are grouped based on, and different characteristic groups are obtained;Based on different characteristic groups, the relationship between signal feature and vital sign is analyzed;Driver's vital sign is monitored based on the relationship between signal feature and vital sign;The present application is used to solve the existing driver vital sign monitoring technology still has the problem that multiple contact type sensing devices are used to monitor the vital sign of driver, which affects the driving safety of driver.

Description

Technical Field

[0001] The present invention relates to the technical field of driver vital sign monitoring, and in particular to an ultra-wideband bioradar vital sign monitoring method, device and medium. Background Art

[0002] Driver vital signs monitoring technology refers to the technology that monitors the driver's vital signs during driving through various monitoring technologies. This type of technology needs to ensure that the monitoring device does not affect the driver's driving process, while being able to protect the driver's driving safety. When abnormalities in the driver's vital signs are detected, the intelligent driving system can be used to send the driver to a safe place to enhance driving safety.

[0003] Existing driver vital signs monitoring technology usually uses contact monitoring instruments to monitor the monitored objects in real time, but there are many scenarios in life that are not suitable for contact monitoring methods, such as patients with large-area burns and patients with highly infectious diseases. During driving, if you want to monitor the driver's vital signs, you also need a non-contact monitoring method. If a contact monitoring method is used, it may affect the driver's driving comfort and increase driving risks to a certain extent. For example, in a patent application with publication number CN114469026A, a driver vital signs monitoring method and system are disclosed. This solution is to monitor the driver's vital signs through contact sensing equipment, and contact sensing equipment may cause discomfort to the driver, increasing driving risks. Existing driver vital signs monitoring technology also has the problem of using multiple contact sensing devices to monitor the driver's vital signs, which causes a certain impact on the driver's driving safety. Summary of the Invention

[0004] The present invention aims to solve one of the technical problems in the prior art to at least a certain extent, by installing an ultra-wideband bioradar on the door of a car, using the ultra-wideband bioradar to transmit a short pulse signal to monitor the driver, obtaining a monitoring signal by receiving the reflected signal, and then filtering the monitoring signal to obtain a vital sign signal, and then extracting the signal characteristics of the vital sign signal, based on the feature grouping and the actual breathing frequency obtained by monitoring, analyzing the deviation parameter between the frequency feature value and the actual breathing frequency, and then correcting the frequency feature value of the feature grouping based on the deviation parameter to obtain a calibration frequency value, and then analyzing the relationship between the signal feature and the heart rate based on the calibration frequency value and the actual heart rate, and analyzing the relationship between the signal feature and the heart rate based on different feature groups, and finally monitoring the driver's vital signs based on the relationship between the signal feature and the vital signs, so as to solve the problem that the existing driver vital sign monitoring technology still has the problem of using multiple contact sensing devices to monitor the driver's vital signs, resulting in a certain impact on the driver's driving safety.

[0005] To achieve the above objectives, in a first aspect, the present application provides an ultra-wideband bioradar vital sign monitoring method, comprising the following steps:

[0006] An ultra-wideband bio-radar is installed on the door of the car, which transmits short pulse signals to monitor the driver and obtains monitoring signals by receiving reflected signals.

[0007] Filter the monitoring signal to obtain the vital sign signal;

[0008] Extracting the signal features of the vital signs signal, grouping them based on different signal features to obtain different feature groups;

[0009] Analyze the relationship between signal characteristics and vital signs based on different feature groups;

[0010] The driver's vital signs are monitored based on the relationship between signal characteristics and vital signs.

[0011] Furthermore, an ultra-wideband bio-radar is installed on the door of the car, and the ultra-wideband bio-radar is used to transmit a short pulse signal to monitor the driver. The monitoring signal is obtained by receiving the reflected signal, which includes the following sub-steps:

[0012] Mark the point of the steering wheel closest to the driver as the observation direction point;

[0013] Consider the car door as a plane, named the vertical plane, and draw a straight line perpendicular to the vertical plane with the observation direction point as the endpoint, named the observation direction line;

[0014] Mark the perpendicular point between the observation direction straight line and the vertical plane as the installation perpendicular point;

[0015] Mark the plane passing through the intersection of the door and the window and parallel to the ground as the horizontal plane;

[0016] With the installation vertical point as the endpoint, draw a straight line perpendicular to the horizontal plane, and mark the vertical point between it and the horizontal plane as the radar installation point;

[0017] Install ultra-wideband bio-radar at radar installation sites;

[0018] Ultra-wideband bio-radar is used to transmit short pulse signals to monitor the driver, and the monitoring signal is obtained by receiving the reflected signal.

[0019] Furthermore, the monitoring signal is filtered using Butterworth filtering technology and MTI harmonic suppression technology, and the vital sign signal is obtained after the monitoring signal is processed.

[0020] Furthermore, extracting the signal features of the vital signs signals and grouping them based on different signal features to obtain different feature groups includes the following sub-steps:

[0021] A first number of volunteers are selected and continuously monitored to obtain a plurality of vital sign signals, wherein the vital sign signals are presented in the form of waves, and the duration of each vital sign signal is a first acquisition duration; and the actual respiratory rate and actual heart rate of the volunteers are simultaneously monitored by a monitoring instrument, wherein the vital sign signals correspond to the actual respiratory rate and actual heart rate, and each vital sign signal corresponds to a plurality of actual respiratory rates and actual heart rates;

[0022] Obtain the frequency of the vital sign signal and mark it as the frequency characteristic value;

[0023] Get the amplitude of the peak and trough, marking them as signal peak and signal trough respectively;

[0024] The signal peaks and valleys are numbered from left to right, respectively by the symbols F n and G n Indicates, where n is a positive integer and n is the sequence number of F and G;

[0025] Calculate F n -G n , mark the calculation result as the fluctuation characteristic value, through the symbol Q n express;

[0026] The frequency characteristic value and the fluctuation characteristic value together constitute the signal feature;

[0027] Signal features are grouped according to frequency eigenvalues ​​to obtain several feature groups.

[0028] Furthermore, based on different feature groups, analyzing the relationship between signal features and vital signs includes the following sub-steps:

[0029] Based on the feature grouping and the actual respiratory frequency obtained by monitoring, the deviation parameter between the frequency feature value and the actual respiratory frequency is analyzed;

[0030] The frequency characteristic values ​​of the feature groups are corrected based on the deviation parameters to obtain the calibrated frequency values. Then, based on the calibrated frequency values ​​and the actual heart rate, the relationship between the signal characteristics and the heart rate is analyzed.

[0031] Furthermore, based on the feature grouping and the actual respiratory frequency obtained by monitoring, analyzing the deviation parameter between the frequency feature value and the actual respiratory frequency includes the following sub-steps:

[0032] The feature groups are sorted and numbered in ascending order of frequency eigenvalues, and the symbol P is used to represent the feature groups. mIndicates, where m is a positive integer and m is the serial number of P. The feature group includes several actual respiratory frequencies, which are numbered and represented by the symbol B(m,i), where i is a positive integer and (m,i) is the serial number of B. B(m,i) represents P m The actual respiratory rate in

[0033] The deviation parameter calculation formula is used to calculate P m and B i Perform calculations to obtain deviation parameters;

[0034] The deviation parameter calculation formula is configured as follows: Among them, DP is the bias parameter, P m Corresponding feature group P m The frequency characteristic value of , max(m) is the maximum value of m, and max(i) is the maximum value of i.

[0035] Furthermore, the frequency characteristic values ​​of the characteristic groups are corrected based on the deviation parameters to obtain a calibrated frequency value, and then based on the calibrated frequency value and the actual heart rate, the relationship between the signal characteristics and the heart rate is analyzed, including the following sub-steps:

[0036] Multiply the frequency characteristic value of each feature group by DP to obtain the calibration frequency value, which is expressed by the symbol W m Indicates that, where m and P m The m in corresponds to

[0037] Calculate P m Medium fluctuation eigenvalue Q n The average value, marked as U m , where m and P m The m in corresponds to

[0038] Calculate W m ×U m , mark the calculation result as the characteristic reference value;

[0039] Each feature group includes several actual heart rates, and a plane rectangular coordinate system is established with the feature reference value on the X axis and the actual heart rate on the Y axis, which is named as the respiratory heart rate influence distribution map. The actual heart rates in the feature group are entered into the respiratory heart rate influence distribution map according to the calibration frequency value;

[0040] The respiratory-heart rate influence distribution map was processed by discrete regression, and the signal characteristics were calculated by fitting, which was named the characteristic heart rate relationship function.

[0041] Furthermore, monitoring the driver's vital signs based on the relationship between the signal characteristics and the vital signs includes the following sub-steps:

[0042] Monitor the driver's vital signs in real time, extract the frequency characteristic value and the fluctuation characteristic value of the vital signs, and mark them as the frequency real-time characteristic value and the fluctuation real-time characteristic value respectively;

[0043] Multiply the frequency real-time characteristic value by DP to obtain the real-time calibration frequency, and multiply the real-time calibration frequency by the fluctuation real-time characteristic value to obtain the characteristic real-time reference value;

[0044] Substitute the characteristic real-time reference value into the characteristic heart rate relationship function, and mark the calculation result as the reference heart rate;

[0045] The real-time frequency characteristic value is used as the driver's real-time breathing frequency, and the reference heart rate is used as the driver's real-time heart rate and uploaded to the monitoring platform.

[0046] In a second aspect, the present application provides an electronic device comprising a processor and a memory, wherein the memory stores computer-readable instructions. When the computer-readable instructions are executed by the processor, the steps in the above method are performed.

[0047] In a third aspect, the present application provides a storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the steps in the above method are performed.

[0048] The present invention has the following beneficial effects: By installing an ultra-wideband bio-radar on a car door, the ultra-wideband bio-radar is used to transmit short pulse signals to monitor the driver. The advantage of installing the ultra-wideband bio-radar on the car door is that it can not only avoid blocking the driver's line of sight, but also more clearly extract the fluctuations of the driver's chest cavity, thereby improving the accuracy and effectiveness of the driver's vital signs monitoring.

[0049] The present invention obtains a monitoring signal by receiving a reflected signal, and then performs filtering processing on the monitoring signal to obtain a vital sign signal, and then extracts the signal feature of the vital sign signal, analyzes the deviation parameter between the frequency feature value and the actual respiratory frequency based on the feature grouping and the actual respiratory frequency obtained by monitoring, and then corrects the frequency feature value of the feature grouping based on the deviation parameter to obtain a calibrated frequency value. The advantage is that, since the driver needs to control the steering wheel with both hands during driving, and the movement of both hands will affect the fluctuation of the driver's chest, the data obtained by direct monitoring is inaccurate. It is necessary to analyze and eliminate the influence of the hand movement on the fluctuation of the driver's chest through experiments to obtain a more accurate respiratory frequency of the driver, thereby improving the accuracy and rationality of the driver's vital sign monitoring.

[0050] The present invention analyzes the relationship between signal characteristics and heart rate based on the calibration frequency value and the actual heart rate, analyzes the relationship between signal characteristics and vital signs based on different feature groups, and finally monitors the driver's vital signs based on the relationship between signal characteristics and vital signs. The advantage is that the human heart rate is related to the respiratory rate and the degree of chest fluctuation. Generally, the higher the human respiratory rate and the greater the degree of chest fluctuation, the higher the heart rate. By experimentally analyzing the relationship between the driver's heart rate and the above, the driver's heart rate can be monitored through a contactless monitoring method, thereby improving the accuracy and effectiveness of the driver's vital signs monitoring. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] Figure 1 is a flow chart of the steps of the method of the present invention;

[0052] Figure 2 A simulated image of the first perspective of the driver in the driving position of the present invention;

[0053] Figure 3 This is a schematic diagram of the present invention in which the vehicle door is regarded as a plane;

[0054] Figure 4 Schematic diagram of the installation vertical point, observation direction straight line and the junction of the door and window of the present invention;

[0055] Figure 5 A schematic diagram of a radar installation point of the present invention;

[0056] Figure 6 This is a schematic diagram of the ultra-wideband bioradar of the present invention after installation;

[0057] Figure 7 is the physical sign signal of the present invention;

[0058] Figure 8 A schematic diagram of the numbering of signal peaks and signal valleys of the present invention;

[0059] Figure 9 A schematic diagram of grouping signal features according to frequency feature values ​​according to the present invention;

[0060] Figure 10 This is a distribution diagram of the impact of breathing and heart rate of the present invention. DETAILED DESCRIPTION

[0061] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0062] Example 1, please refer to Figure 1 As shown, the present application provides an ultra-wideband bioradar vital sign monitoring method, comprising the following steps:

[0063] Step S1: Install an ultra-wideband bio-radar on the door of a car, use the ultra-wideband bio-radar to emit short pulse signals to monitor the driver, and obtain a monitoring signal by receiving the reflected signal. Step S1 includes the following sub-steps:

[0064] See also Figure 2 As shown, in step S101, the point of the steering wheel closest to the driver is marked as the observation direction point;

[0065] See also Figures 3 and 4 As shown, in step S102, the vehicle door is regarded as a plane, named the vertical plane, and a straight line is drawn perpendicular to the vertical plane with the observation direction point as the endpoint, named the observation direction line; the selected vehicle door is the driver's door, the vertical plane is a plane perpendicular to the horizontal plane, and is parallel to the vehicle's central axis, the vehicle's central axis is the line connecting the midpoint of the front end of the vehicle to the midpoint of the rear end of the vehicle, and the two midpoints are at the same horizontal height. The spatial position of the vertical plane is located inside the vehicle; the above definition of the vertical plane is to be able to find an installation point for the ultra-wideband bio-radar on the inside of the vehicle door;

[0066] Step S103, marking the perpendicular point between the observation direction straight line and the vertical plane as the installation perpendicular point;

[0067] Step S104: Mark the plane passing through the junction of the door and the window and parallel to the ground as a horizontal plane;

[0068] See also Figures 5 and 6 As shown, in step S105, a straight line is drawn perpendicular to the horizontal plane with the installation vertical point as the endpoint, and the vertical point between the straight line and the horizontal plane is marked as the radar installation point;

[0069] Step S106, installing an ultra-wideband bio-radar at the radar installation point;

[0070] Step S107, using an ultra-wideband bio-radar to transmit a short pulse signal to monitor the driver, and obtaining a monitoring signal by receiving the reflected signal;

[0071] In the specific implementation, the position of the observation direction point is as follows: Figure 2 As shown, Figure 2 This is a simulated image of the driver's first-person perspective in the driver's seat. The observation direction point is the point on the steering wheel closest to the driver, which can ensure that the driver's chest will not pass through the observation direction point; the car door is regarded as a plane such as Figure 3 As shown, the installation vertical point is Figure 2 The corresponding position in Figure 4 As shown, Figure 4 The intersection of the door and the window is also indicated. By drawing and finding the vertical point, the radar installation point is obtained. Figure 5 As shown in the figure, after the ultra-wideband bio-radar is installed, Figure 6 As shown, an ultra-wideband bio-radar is used to transmit a short pulse signal to monitor the driver, and a monitoring signal is obtained by receiving the reflected signal.

[0072] See also Figure 7 As shown, step S2, filtering the monitoring signal to obtain a vital sign signal; Butterworth filtering technology and MTI harmonic suppression technology are used to filter the monitoring signal, and the vital sign signal is obtained after processing the monitoring signal;

[0073] In the specific implementation, the existing technology is used to filter the monitoring signal. The Butterworth filtering technology and MTI harmonic suppression technology can be used to extract the vital signs of the chest movement of the monitored object in the monitoring signal, such as Figure 7 shown.

[0074] Step S3, extracting the signal features of the vital sign signal, and grouping them based on different signal features to obtain different feature groups; Step S3 includes the following sub-steps:

[0075] Step S301: Select a first number of volunteers and continuously monitor the first number of volunteers to obtain a plurality of vital sign signals, wherein the vital sign signals are presented in the form of waves, and the duration of each vital sign signal is a first acquisition duration; simultaneously, the actual respiratory rate and actual heart rate of the volunteers are monitored by a monitoring instrument, wherein the vital sign signals correspond to the actual respiratory rate and actual heart rate, and each vital sign signal corresponds to a plurality of actual respiratory rates and actual heart rates;

[0076] In a specific implementation, the first number is set to 100. 100 volunteers are selected in order to collect signal characteristics of drivers under different driving conditions, which are used as basic data for subsequent judgment and analysis. During the experiment, the volunteers are seated in a driving simulator and asked to respond to all driving scenarios in a game-like manner. During this period, their vital signs are collected. The first collection time is set to 30 seconds. Within 30 seconds, the driver's chest can rise and fall multiple times to form a vital sign signal. There is no fixed value for the first number and the first collection time. Generally, the larger the first number, the better. The maximum first collection time does not exceed 60 seconds and the minimum does not exceed 30 seconds. During the test, the actual respiratory rate and actual heart rate of the volunteers are collected through a contact-type respiratory sensor and a heart rate sensor. Generally, the default collection frequency of the respiratory sensor and the heart rate sensor is 2 seconds, that is, data is collected every two seconds. Therefore, 15 actual respiratory rates and actual heart rates will be collected within 30 seconds. These 15 actual respiratory rates and actual heart rates correspond to the collected vital signs signals, that is, one vital sign signal corresponds to a set of actual respiratory rates and actual heart rates.

[0077] Step S302, obtaining the frequency of the vital sign signal, marked as a frequency characteristic value;

[0078] In the specific implementation, the frequency characteristic value obtained is 20 times / min. The frequency characteristic value is the number of peaks in the vital sign signal. The peak represents the moment when the volunteer's chest cavity bulges to the maximum after inhalation and is ready to exhale. Therefore, the number of peaks can represent the number of times the volunteer inhales. Each inhalation represents the completion of a successive breath. Figure 7 There are 10 peaks in the CCP, but Figure 7 There is only 30s of data, which can be converted to minutes as 20 times / min;

[0079] Step S303, obtaining the amplitudes of the peaks and valleys, marking them as signal peaks and signal valleys respectively;

[0080] See also Figure 8 As shown, in step S304, the signal peaks and signal valleys are numbered in order from left to right, respectively by symbols F n and G n Indicates, where n is a positive integer and n is the sequence number of F and G. Usually, the number of selected peaks and troughs is equal, so a unified subscript is used;

[0081] Step S305, calculate F n -G n , mark the calculation result as the fluctuation characteristic value, through the symbol Q n express;

[0082] Step S306, the frequency characteristic value and the fluctuation characteristic value jointly constitute the signal characteristic;

[0083] Please refer to Figure 9 As shown in the figure, step S307, the signal characteristics are grouped according to the frequency characteristic value, and a plurality of characteristic groups are obtained;

[0084] In a specific implementation, F n and G n The signal peak value and the signal valley value corresponding to the number of F Figure 8 Take F1 and G1 as an example, F1 is 0.009, G1 is -0.0038, and the fluctuation characteristic value Q1= F1-G1=0.0128 is calculated. Similarly, Q2 to Q 10 are calculated, and 10 fluctuation characteristic values are obtained. The 10 fluctuation characteristic values and the frequency characteristic value jointly constitute the signal characteristic. According to the different frequency characteristic values, the signal characteristics are grouped. Each characteristic group corresponds to a frequency characteristic value, and the characteristic group includes a plurality of fluctuation characteristic values of the signal characteristics. The process of grouping the signal characteristics according to the frequency characteristic value is shown in Figure 9 , Figure 9 The left side in the figure represents different physical signals monitored, and the right side represents the characteristic groups grouped based on the frequency characteristic value. The characteristic groups also include the actual respiratory rate and the actual heart rate, but they are not marked in Figure 9 . Take Figure 9 as an example. The frequency characteristic value corresponding to the first characteristic group is "frequency characteristic value 1". The signal characteristics with the frequency characteristic value "frequency characteristic value 1" are all recorded in the first characteristic group, and "fluctuation characteristic value 11" and "fluctuation characteristic value 12" belong to the first characteristic group. "Fluctuation characteristic value 11" and "fluctuation characteristic value 12" correspond to a group of actual respiratory rate and actual heart rate respectively, so their actual respiratory rate and actual heart rate also belong to the first characteristic group.

[0085] Step S4, based on different characteristic groups, analyze the relationship between signal characteristics and vital signs; step S4 includes the following sub-steps:

[0086] Step S401, based on the characteristic group and the actual respiratory rate monitored, analyze the deviation parameter between the frequency characteristic value and the actual respiratory rate;

[0087] Step S401 includes the following sub-steps:

[0088] Step S401.1, sort and number the characteristic groups in order of frequency characteristic value from small to large, through symbol P mIndicates, where m is a positive integer and m is the serial number of P. The feature group includes several actual respiratory frequencies, which are numbered and represented by the symbol B(m,i), where i is a positive integer and (m,i) is the serial number of B. B(m,i) represents P m The actual respiratory rate in

[0089] Step S401.2: calculate the deviation parameter using the formula m and B i Perform calculations to obtain deviation parameters;

[0090] The deviation parameter calculation formula is configured as Among them, DP is the bias parameter, P m Corresponding feature group P m The frequency characteristic value of , max(m) is the maximum value of m, and max(i) is the maximum value of i;

[0091] In the specific implementation, there are 10 feature groups in this embodiment, so the feature groups are sorted and numbered in the order of frequency feature values ​​from small to large to obtain P1 to P 10 , 1≤m≤10, where P1 to P 10 The frequency characteristic values ​​are 16 times / min, 17 times / min, 18 times / min, 19 times / min, 20 times / min, 21 times / min, 22 times / min, 23 times / min, 24 times / min and 25 times / min, respectively. Taking feature group P1 as an example, there are 315 actual respiratory rates in feature group P1 in this embodiment, and B(1,1) to B(1,315) are obtained by numbering. Due to the large amount of data, the specific values ​​of the various data in the deviation parameter calculation formula are no longer listed in this embodiment, and only the final calculation result is given. The deviation parameter DP obtained by calculation in this embodiment is 0.81, and the calculation result is rounded to two decimal places;

[0092] Step S402, correcting the frequency characteristic values ​​of the characteristic group based on the deviation parameter to obtain a calibrated frequency value, and then analyzing the relationship between the signal characteristics and the heart rate based on the calibrated frequency value and the actual heart rate;

[0093] Step S402 includes the following sub-steps:

[0094] Step S402.1, multiply the frequency characteristic value of each feature group by DP to obtain the calibration frequency value, which is represented by the symbol W m Indicates that, where m and P m The m in corresponds to

[0095] Step S402.2, calculate P m Medium fluctuation eigenvalue Q nThe average value, marked as U m , where m and P m The m in corresponds to

[0096] In the specific implementation, P1 to P 10 The frequency characteristic values ​​of W1 to W2 are 16 times / min, 17 times / min, 18 times / min, 19 times / min, 20 times / min, 21 times / min, 22 times / min, 23 times / min, 24 times / min and 25 times / min, respectively. 10 They are 13 times / min, 14 times / min, 15 times / min, 15 times / min, 16 times / min, 17 times / min, 18 times / min, 19 times / min, 19 times / min and 20 times / min, and the calculation results are all integers. Taking P1 as an example, the fluctuation characteristic values ​​Q1 to Q 10 They are 0.0128, 0.0309, 0.0358, 0.0336, 0.0248, 0.0087, 0.0125, 0.0249, 0.0229 and 0.0132, respectively. The result is U1=0.0220, and the result is rounded to four decimal places.

[0097] Step S402.3, calculate W m ×U m , mark the calculation result as the characteristic reference value;

[0098] See also Figure 10 As shown in step S402.4, each feature group includes several actual heart rates. A rectangular coordinate system is established with the feature reference value as the X-axis and the actual heart rate as the Y-axis, named the respiratory heart rate influence distribution map. The actual heart rates in the feature group are entered into the respiratory heart rate influence distribution map according to the calibration frequency value;

[0099] Step S402.5, performing discrete regression processing on the respiratory heart rate influence distribution map, calculating the signal characteristics by fitting, and naming it the characteristic heart rate relationship function;

[0100] In the specific implementation, W1 is 13 times / min, U1 is 0.0220, and the calculated characteristic reference value is 0.2860, and the calculation result is rounded to four decimal places; taking the characteristic group P1 as an example, the characteristic reference value of P1 is 0.2860. Assuming that P1 includes 4 actual heart rates, namely 63 times / min, 65 times / min, 65 times / min and 67 times / min, then the corresponding 4 coordinate points in the respiratory heart rate influence distribution map are (0.286, 63), (0.286, 65), (0.286, 65) and (0.286, 67); and so on, the respiratory influence distribution map is constructed as follows Figure 10As shown, the characteristic heart rate relationship function obtained by linear fitting in discrete regression is Y=68.366×X+45.618, where Y is the actual heart rate and X is the characteristic reference value.

[0101] Step S5, monitoring the driver's vital signs based on the relationship between the signal characteristics and the vital signs; Step S5 includes the following sub-steps:

[0102] Step S501: monitoring the driver's vital signs in real time, extracting the frequency characteristic value and the fluctuation characteristic value of the vital signs, and marking them as the frequency real-time characteristic value and the fluctuation real-time characteristic value respectively;

[0103] Step S502: multiply the frequency real-time characteristic value by DP to obtain a real-time calibration frequency, and multiply the real-time calibration frequency by the fluctuation real-time characteristic value to obtain a characteristic real-time reference value;

[0104] Step S503, substituting the characteristic real-time reference value into the characteristic heart rate relationship function, and marking the calculation result as the reference heart rate;

[0105] Step S504: Using the real-time frequency characteristic value as the driver's real-time breathing frequency and the reference heart rate as the driver's real-time heart rate and uploading them to the monitoring platform;

[0106] In the specific implementation, the frequency characteristic value of the driver monitored is 16 times / min, and the fluctuation characteristic value is 0.0224. The real-time frequency characteristic value is multiplied by DP to obtain the real-time calibration frequency of 16 times / min×0.81=13 times / min. The calculation result is retained as an integer. The real-time reference value of the characteristic is calculated to be 13×0.0224=0.2912. X=0.2912 is substituted into Y=68.366×X+45.618 to obtain the reference heart rate of 66 times / min. The calculation result is retained as an integer. Through contactless monitoring, the real-time breathing frequency of the driver is 13 times / min, and the real-time heart rate is 66 times / min. By monitoring the driver's breathing frequency and heart rate, the driver's physical health status can be effectively assessed. Integrating it into the intelligent driving system can remind the driver when his physical health is abnormal, and the vehicle can be stopped to the roadside in time when the driver operates abnormally.

[0107] In Example 2, the present application provides an electronic device that may include: a processor, a communication interface, a memory, and a communication bus, wherein the processor, the communication interface, and the memory communicate with each other via the communication bus. The memory stores computer-readable instructions, and the processor may call instructions from the memory. When the computer-readable instructions are executed by the processor, the processor performs steps such as those in an ultra-wideband bioradar vital sign monitoring method to achieve the following functions: using an ultra-wideband bioradar to transmit short pulse signals to monitor the driver; filtering the monitoring signals to obtain vital sign signals; extracting signal features of the vital sign signals, grouping them based on different signal features to obtain different feature groups; analyzing the relationship between the signal features and vital signs based on the different feature groups; and monitoring the driver's vital signs based on the relationship between the signal features and vital signs.

[0108] In addition, the logical instructions in the above-mentioned memory can be implemented in the form of a software functional unit and can be stored in a computer-readable storage medium when sold or used as an independent product. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product, which is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes: various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk.

[0109] Example 3. The present application also provides a computer program product, which includes a computer program stored on a computer-readable storage medium, and the computer program includes program instructions. When the program instructions are executed by a computer, the computer can execute an ultra-wideband bioradar vital signs monitoring method provided by the above methods, which includes: using an ultra-wideband bioradar to transmit a short pulse signal to monitor the driver; filtering the monitoring signal to obtain a vital sign signal; extracting signal features of the vital sign signal, grouping it based on different signal features to obtain different feature groups; based on different feature groups, analyzing the relationship between signal features and vital signs; monitoring the driver's vital signs based on the relationship between signal features and vital signs.

[0110] Example 4. The present application also provides a computer-readable storage medium. The present application provides a storage medium on which a computer program is stored. When the computer program is executed by a processor, the steps in the above ultra-wideband bioradar vital signs monitoring method are executed to achieve the following functions: use an ultra-wideband bioradar to transmit a short pulse signal to monitor the driver; filter the monitoring signal to obtain a vital sign signal; extract the signal features of the vital sign signal, group it based on different signal features, and obtain different feature groups; based on different feature groups, analyze the relationship between the signal features and the vital signs; and monitor the driver's vital signs based on the relationship between the signal features and the vital signs.

[0111] Through the description of the above embodiments, the embodiments of the present invention can be provided as methods, systems or computer program products. Based on this understanding, the above technical solutions, in essence or in other words, the part that contributes to the prior art, can be embodied in the form of a software product, which can be stored in a computer-readable storage medium, such as ROM / RAM, a magnetic disk, an optical disk, etc., and includes a number of instructions for enabling a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment or certain parts of the embodiment.

[0112] In the embodiments provided in this application, it should be understood that the disclosed system or method can be implemented in other ways. The embodiments described above are merely illustrative. For example, the division of modules or units is only a logical function division. There may be other division methods in actual implementation. For example, multiple modules or units can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some communication interfaces, and the indirect coupling or communication connection of systems, modules and units can be electrical, mechanical or other forms.

[0113] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. An ultra-wideband bioradar vital signs monitoring method, characterized in that: The steps include: An ultra-wideband bio-radar is installed on the door of the car, which transmits short pulse signals to monitor the driver and obtains monitoring signals by receiving reflected signals. Filter the monitoring signal to obtain the vital sign signal; Extracting the signal features of the vital signs signal, grouping them based on different signal features to obtain different feature groups; Extracting the signal characteristics of the vital sign signal specifically includes: obtaining the frequency of the vital sign signal, marking it as the frequency characteristic value; obtaining the amplitude of the peak and the trough, marking them as the signal peak value and the signal trough value respectively; numbering the signal peak value and the signal trough value in order from left to right, respectively by the symbol F n and G n Indicates, where n is a positive integer and n is the serial number of F and G; calculate F n -G n , mark the calculation result as the fluctuation characteristic value, through the symbol Q n Indicates that the feature groups are sorted and numbered in ascending order of frequency eigenvalues, and the symbol P is used. m Represents, where m is a positive integer and m is the serial number of P; Analyze the relationship between signal characteristics and vital signs based on different feature groups; Monitoring the driver's vital signs based on the relationship between signal characteristics and vital signs; Analyzing the relationship between signal features and vital signs based on different feature groups includes the following sub-steps: Based on the feature grouping and the actual respiratory frequency obtained by monitoring, the deviation parameter between the frequency feature value and the actual respiratory frequency is analyzed; Based on the deviation parameter, the frequency characteristic value of the feature group is corrected to obtain the calibration frequency value, which is expressed by the symbol W m Indicates that, where m and P m The m in corresponds to; calculate P m Medium fluctuation eigenvalue Q n The average value, marked as U m , where m and P m The m in corresponds to; calculate W m ×U m , mark the calculation result as the characteristic reference value; Analyze the relationship between characteristic reference values ​​and actual heart rate.

2. The ultra-wideband bioradar vital sign monitoring method according to claim 1, characterized in that: An ultra-wideband bio-radar is installed on the door of a car, and a short pulse signal is emitted by the ultra-wideband bio-radar to monitor the driver. The monitoring signal is obtained by receiving the reflected signal, which includes the following sub-steps: Mark the point of the steering wheel closest to the driver as the observation direction point; Consider the car door as a plane, named the vertical plane, and draw a straight line perpendicular to the vertical plane with the observation direction point as the endpoint, named the observation direction line; Mark the perpendicular point between the observation direction straight line and the vertical plane as the installation perpendicular point; Mark the plane passing through the intersection of the door and the window and parallel to the ground as the horizontal plane; With the installation vertical point as the endpoint, draw a straight line perpendicular to the horizontal plane, and mark the vertical point between it and the horizontal plane as the radar installation point; Install ultra-wideband bio-radar at radar installation sites; Ultra-wideband bio-radar is used to transmit short pulse signals to monitor the driver, and the monitoring signal is obtained by receiving the reflected signal.

3. The ultra-wideband bioradar vital sign monitoring method according to claim 2, characterized in that: The monitoring signal is filtered using Butterworth filtering technology and MTI harmonic suppression technology, and the vital sign signal is obtained after the monitoring signal is processed.

4. The ultra-wideband bioradar vital sign monitoring method according to claim 3, characterized in that: Extraction of vital signs includes: A first number of volunteers are selected and continuously monitored to obtain a number of vital sign signals, which are presented in the form of waves, and the duration of each vital sign signal is the first acquisition duration; at the same time, the actual respiratory rate and actual heart rate of the volunteers are monitored by a monitoring instrument, and the vital sign signals correspond to the actual respiratory rate and the actual heart rate, and each vital sign signal corresponds to a number of actual respiratory rates and actual heart rates.

5. The ultra-wideband bioradar vital sign monitoring method according to claim 4, characterized in that: Analyzing the deviation parameter between the frequency characteristic value and the actual respiratory frequency based on the feature grouping and the monitored actual respiratory frequency includes the following sub-steps: The feature groups are sorted and numbered in ascending order of frequency feature values. The feature groups include several actual respiratory frequencies, which are numbered and represented by the symbol B(m,i), where i is a positive integer and (m,i) is the sequence number of B. B(m,i) represents P m The actual respiratory rate in The deviation parameter calculation formula is used to calculate P m And B(m,i) are calculated to obtain the deviation parameter; The deviation parameter calculation formula is configured as follows: ; Where DP is the deviation parameter, P m Corresponding feature group P m The frequency characteristic value of , max(m) is the maximum value of m, and max(i) is the maximum value of i.

6. The ultra-wideband bioradar vital sign monitoring method according to claim 5, characterized in that: The calibration frequency value is obtained by multiplying the frequency feature value of each feature group by DP; Analyzing the relationship between the characteristic reference value and the actual heart rate specifically includes: each characteristic group includes a number of actual heart rates, establishing a plane rectangular coordinate system with the characteristic reference value as the X-axis and the actual heart rate as the Y-axis, and naming it as a respiratory heart rate influence distribution map, and entering the actual heart rates in the characteristic group into the respiratory heart rate influence distribution map according to the characteristic reference value; The respiratory-heart rate influence distribution map was processed by discrete regression, and the signal characteristics were calculated by fitting, which was named the characteristic heart rate relationship function.

7. The ultra-wideband bioradar vital sign monitoring method according to claim 6, characterized in that: Monitoring the driver's vital signs based on the relationship between signal characteristics and vital signs includes the following sub-steps: Monitor the driver's vital signs in real time, extract the frequency characteristic value and the fluctuation characteristic value of the vital signs, and mark them as the frequency real-time characteristic value and the fluctuation real-time characteristic value respectively; Multiply the frequency real-time characteristic value by DP to obtain the real-time calibration frequency, and multiply the real-time calibration frequency by the fluctuation real-time characteristic value to obtain the characteristic real-time reference value; Substitute the characteristic real-time reference value into the characteristic heart rate relationship function, and mark the calculation result as the reference heart rate; The real-time calibration frequency is used as the driver's real-time breathing rate, and the reference heart rate is used as the driver's real-time heart rate and uploaded to the monitoring platform.

8. An electronic device, characterized in that: The method comprises a processor and a memory, wherein the memory stores computer-readable instructions. When the computer-readable instructions are executed by the processor, the steps in the method according to any one of claims 1 to 7 are executed.

9. A storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are executed.

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